Unique characterisability and learnability of Temporal Instance Queries

Fortin, M. and Konev, B. and Ryzhikov, Vladislav and Savateev, Yury and Wolter, F. and Zakhariyashchev, Michael (2022) Unique characterisability and learnability of Temporal Instance Queries. In: 19th International Conference on Principles of Knowledge Representation and Reasoning, KR 2022, 31 Jul - 05 Aug 2022, Haifa, Israel.

Abstract

We aim to determine which temporal instance queries can be uniquely characterised by a (polynomial-size) set of positive and negative temporal data examples. We start by considering queries formulated in fragments of propositional linear temporal logic \LTL{} that correspond to conjunctive queries (CQs) or extensions thereof induced by the until operator. Not all of these queries admit polynomial characterisations but by restricting them further to path-shaped queries we identify natural classes that do. %imposing a further restriction to path-shaped queries we identify natural classes that do. We then investigate how far the obtained characterisations can be lifted to temporal knowledge graphs queried by 2D languages combining LTL with concepts in description logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope of description logic constructors can destroy polynomial characterisability, we obtain general transfer results for the case when description logic constructors are within the scope of temporal operators. Finally, we apply our characterisations to establish (polynomial) learnability of temporal instance queries using membership queries in the active learning framework.

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